قارن الطرق

	تحليل المكونات الرئيسية باستخدام النواة (Kernel PCA)	آلة المتجهات الداعمة (التصنيف)
المجال	تعلم الآلة	تعلم الآلة
العائلة	Latent structure	Machine learning
سنة النشأة	1998	1995
صاحب الطريقة	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Cortes, C. & Vapnik, V.
النوع	Nonlinear dimensionality reduction via kernel trick	Maximum-margin classifier (kernel method)
المصدر التأسيسي	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
الأسماء البديلة	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
ذات صلة	5	5
الملخص	Kernel Principal Component Analysis (Kernel PCA) is a nonlinear dimensionality-reduction method introduced by Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller in 1997–1998. It extends classical linear PCA to curved, non-linear data manifolds by implicitly mapping input data into a high-dimensional feature space via a kernel function, then performing standard PCA in that space — all without ever computing the mapping explicitly.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.